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Quality-Diversity Learning Enabled Multi-Alternative Unit Commitment Optimization
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DOI：10.1109/TPWRS.2025.3631269.png)
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Abstract
En 中文
This letter proposes a novel quality-diversity learning (QDL) method for multi-alternatives unit commitment (UC) optimization. Existing UC methods focus solely on finding a single global optimum, neglecting insights from alternative solutions with competitive performance. In contrast, QDL maintains a multi-cell agent archive populated with multiple high-performing UC policies, each sharing the same objective while evolving to explore distinct behavioral regions, enabling simultaneous optimization of solution quality and diversity. The resulting diverse solutions catering to various dispatch preferences not only enhance operational preparedness, but also allow rapid retrieval of alternatives if feasibility tests fail. Case studies on several standard test systems confirm the effectiveness of the method.
Keywords:
Unit commitment
quality-diversity method
population-based learning
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